{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\k1801607\Dropbox\Trump2_accepted\supplementary.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 4 Jul 2019, 18:09:25
{txt}
{com}. 
. clear all
{res}{txt}
{com}. use  data.dta
{txt}
{com}. 
. set more off
{txt}
{com}. 
. *Alternative modelling
. ***** coasened exact matching 
. ssc install cem
{txt}checking {hilite:cem} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. keep if interval==1
{txt}(44,774 observations deleted)

{com}. 
. imb age  female domicil child minority edu hincfel uemp3m  voting , treatment (trump) //identify unbalanced covariated
{res}
{txt}Multivariate L1 distance: {res}.74831351

{txt}Univariate imbalance:

               L1     mean      min      25%      50%      75%      max
     age  {res} .04046  -.42212        0        0        0       -1       -1
{txt}  female  {res} .00742   .00742        0        0        0        0        0
{txt} domicil  {res} .03987  -.11138        0       -1        0        0        0
{txt}   child  {res} .00188  -.00188        0        0        0        0        0
{txt}minority  {res} .00186   .00186        0        0        0        0        0
{txt}     edu  {res} .03167   .08961        0        0        0        0        0
{txt} hincfel  {res}  .0074  -.01196        0        0        0        0        0
{txt}  uemp3m  {res} .01157   .01157        0        0        0        0        0
{txt}  voting  {res} .00318   .00318        0        0        0        0        0
{txt}
{com}. cem age(15 20 24 30 34 40 44 50 54 60 64 70 74 80) domicil(#0)  country(#0), treatment (trump) //match on unbalanced covariated (according to scott-break method)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}865
{txt}Number of matched strata: {res}648

           {txt}   0     1
      All  {res}4253  3818
{txt}  Matched  {res}4004  3645
{txt}Unmatched  {res} 249   173


{txt}Multivariate L1 distance: {res}.18318437

{txt}Univariate imbalance:

              L1     mean      min      25%      50%      75%      max
    age  {res} .02914  -.03931        0        0        0       -1        .
{txt}domicil  {res}1.6e-15  1.2e-14        0        0        0        0        .
{txt}country  {res}2.6e-15  4.6e-14        0        0        0        0        0
{txt}
{com}. drop if cem_matched==0 //then cut un-matched
{txt}(422 observations deleted)

{com}.  
.  
. quietly: logit  race  trump age squaredage female domicil child minority edu hincfel uemp3m voting i.country [pweight=dweight], cluster(country)
{txt}
{com}. margins, dydx(trump)  // 1st column
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,335
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(race), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0246012{col 26}{space 2} .0075832{col 37}{space 1}    3.24{col 46}{space 3}0.001{col 54}{space 4} .0097383{col 67}{space 3} .0394641
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  
. ***** entropy balancing pooled
. quietly: ebalance  trump age  female domicil child minority edu hincfel  voting uemp3m country, tar(3)
{txt}
{com}. quietly: logit  race  trump age squaredage female domicil child minority edu hincfel  uemp3m voting i.country [pweight=dweight* _webal], cluster(country) 
{txt}
{com}. margins, dydx(trump)  // 2nd column
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,335
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(race), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2}   .02567{col 26}{space 2} .0071123{col 37}{space 1}    3.61{col 46}{space 3}0.000{col 54}{space 4} .0117302{col 67}{space 3} .0396097
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ***** entropy balancing per country
. quietly: ebalance  trump c.age##i.country  i.female##i.country  c.domicil##i.country  i.child##i.country  i.minority##i.country  c.edu##i.country  c.hincfel##i.country i.uemp3m##i.country    i.voting##i.country  country, tar(3)
{txt}
{com}. quietly: logit  race  trump age squaredage female domicil child minority edu hincfel uemp3m voting i.country [pweight=dweight* _webal], cluster(country) 
{txt}
{com}. margins, dydx(trump)  // 3rd column
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,335
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(race), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2}  .024118{col 26}{space 2} .0100159{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .0044871{col 67}{space 3} .0437488
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ***** wild cluster bootstraap
. ssc install clustse
{txt}checking {hilite:clustse} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *But, to use Wild wild cluster bootstraap you need an .ado called "unique", and you need to manually add it to your stata packages ( at C:\ado\plus\c)
. 
. //must create dummy variables cause wild cluster bootstraap does not allow for factor variables (i.country)
. 
. 
. gen at=0
{txt}
{com}. gen be=0
{txt}
{com}. gen ch=0
{txt}
{com}. gen cz=0
{txt}
{com}. gen de=0
{txt}
{com}. gen ee=0
{txt}
{com}. gen fi=0
{txt}
{com}. gen il=0
{txt}
{com}. gen uk=0
{txt}
{com}. gen nl=0
{txt}
{com}. gen no=0
{txt}
{com}. gen si=0
{txt}
{com}. gen se=0
{txt}
{com}. 
. replace at=1 if country==1
{txt}(753 real changes made)

{com}. replace be=1 if country==2
{txt}(371 real changes made)

{com}. replace ch=1 if country==3
{txt}(279 real changes made)

{com}. replace cz=1 if country==4
{txt}(1,963 real changes made)

{com}. replace de=1 if country==5
{txt}(629 real changes made)

{com}. replace ee=1 if country==6
{txt}(667 real changes made)

{com}. replace fi=1 if country==7
{txt}(616 real changes made)

{com}. replace il=1 if country==11
{txt}(561 real changes made)

{com}. replace uk=1 if country==9
{txt}(415 real changes made)

{com}. replace nl=1 if country==13
{txt}(399 real changes made)

{com}. replace no=1 if country==14
{txt}(407 real changes made)

{com}. replace si=1 if country==17
{txt}(286 real changes made)

{com}. replace se=1 if country==18
{txt}(303 real changes made)

{com}. 
. *cgmwildboot race  trump age squaredage female domicil child minority edu hincfel  uemp3m voting at be ch cz ee de fi uk il  nl no se si [pweight= dweight], cluster(country) bootcluster(country) reps(50) seed(999)
.  // 4th column
. 
. ***** wild cluster bootstraap + entropy balancing per country
. 
. *cgmwildboot race  trump age squaredage female domicil child minority edu hincfel  uemp3m voting at be ch cz ee de fi uk il  nl no se si [pweight= _webal*dweight], cluster(country) bootcluster(country) reps(50) seed(999)
.  // 5th column
. 
. 
. 
. *Entropy balanced covariates
. clear all
{res}{txt}
{com}. use  data.dta
{txt}
{com}. 
. keep if  election2016==1 & interval==1
{txt}(44,774 observations deleted)

{com}. quietly:  ebalance  trump  age  female domicil  child minority edu hincfel uemp3m voting country , g(ebw) tar(3)
{txt}
{com}. twoway (kdensity age if trump==1, bw(3)) (kdensity age [aweight=ebw] if trump==0, bw(3)), xtitle("age") legend(label(1 "treated") label(2 "control")) title("Age in control and treatment after entropy balancing")
{res}{txt}
{com}. twoway (kdensity edu if trump==1, bw(3)) (kdensity edu [aweight=ebw] if trump==0, bw(3)), xtitle("education") legend(label(1 "treated") label(2 "control")) title("Education in control and treatment after entropy balancing")
{res}{txt}
{com}. twoway (kdensity domicil if trump==1, bw(3)) (kdensity domicil [aweight=ebw] if trump==0, bw(3)), xtitle("domicile") legend(label(1 "treated") label(2 "control")) title("Domicile in control and treatment after entropy balancing")
{res}{txt}
{com}. twoway (kdensity hincfel if trump==1, bw(3)) (kdensity hincfel [aweight=ebw] if trump==0, bw(3)), xtitle("income") legend(label(1 "treated") label(2 "control")) title("Income in control and treatment after entropy balancing")
{res}{txt}
{com}. 
. *Reachability 
. 
. hist attempts, fraction by(trump)
{res}{txt}
{com}. 
. quietly:logit  race  trump age squaredage female domicil child minority edu hincfel  voting uemp3m i.country  [pweight= dweight],  cluster(country)              
{txt}
{com}. quietly:margins, dydx(  trump)
{txt}
{com}. est store main_regression
{txt}
{com}. 
. quietly: logit  race  trump age attempts squaredage female domicil child minority edu hincfel  voting uemp3m i.country [pweight= dweight], cluster(country)              
{txt}
{com}. quietly: margins, dydx(trump)
{txt}
{com}. est store controlling
{txt}
{com}.  
. quietly: logit  race  trump age  squaredage female domicil child minority edu hincfel  voting uemp3m i.country if attempts<3 [pweight= dweight], cluster(country)              
{txt}
{com}. quietly:margins, dydx(trump)
{txt}
{com}. est store without
{txt}
{com}. 
.  coefplot (main_regression) (controlling) (without), vertical keep (trump) drop(_cons) yline(0)
{res}{txt}
{com}.  
. *Regional imbalance (regressions follow below)
. 
. clear all
{res}{txt}
{com}. use  data.dta
{txt}
{com}. 
. keep if election2016==1
{txt}(27,848 observations deleted)

{com}. 
. 
. histogram regione,  by(trump, col(1))  xtitle("Regions in Austria before (above) and after (below) the election") note("Source: ESS8")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==1, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==1, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Austria. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==2, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==2, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Belgium. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==3, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==3, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Switzerland. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==4, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==4, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Czech Republic . Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==5, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==5, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Germany. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==6, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==6, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Estonia. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==7, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==7, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Finland. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==9, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==9, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("UK. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==13, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==13, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Netherlands. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==14, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==14, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Norway. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==17, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==17, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Sweden. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. twoway (hist regione if trump==0 & country==18, frac width(.25) barw(.35) bfcolor(none) blcolor(gray)) (hist regione if trump==1 & country==18, frac width(.25) barw(.35) bfcolor(none) blcolor(red)), legend(off) xtitle("Slovack Republic. Sample by region Before (grey) and After (red) the election.")
{res}{txt}
{com}. 
. keep if interval==1
{txt}(16,926 observations deleted)

{com}. 
. quietly: ebalance  trump age  female domicil child minority edu hincfel uemp3m voting country, tar(3)
{txt}
{com}. quietly: logit  race  trump age squaredage female domicil child minority edu hincfel  voting uemp3m i.country [pweight= _webal*dweight], cluster(country)              
{txt}
{com}. margins, dydx(  trump)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(race), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0229849{col 26}{space 2} .0077986{col 37}{space 1}    2.95{col 46}{space 3}0.003{col 54}{space 4} .0076999{col 67}{space 3} .0382698
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store regionally_unbalanced
{txt}
{com}.  
. quietly: ebalance  trump age  female domicil child minority edu hincfel uemp3m voting country region, tar(3)
{txt}
{com}. quietly: logit  race  trump age squaredage female domicil child minority edu hincfel  voting uemp3m i.country i.regione [pweight= _webal*dweight], cluster(country)              
{txt}
{com}. margins, dydx(  trump)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,568
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(race), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0211647{col 26}{space 2}  .008878{col 37}{space 1}    2.38{col 46}{space 3}0.017{col 54}{space 4}  .003764{col 67}{space 3} .0385653
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store regionally_balanced
{txt}
{com}. 
. /// shows that controlling for region-imbalance does not change the main treatment effect
> 
.  coefplot (regionally_unbalanced) (regionally_balanced), vertical keep (trump) drop(_cons) yline(0)
{res}{txt}
{com}. 
. 
.  *Alternative dependent variables
. 
. quietly:  ebalance  trump  age  female domicil  child minority edu hincfel uemp3m voting country if election2016==1 & interval==1 , tar(3)
{txt}
{com}. quietly: logit  race  trump age squaredage female domicil child minority edu hincfel  uemp3m voting i.country [pweight= _webal*dweight] if election2016==1 & interval==1, cluster(country)              
{txt}
{com}. est store a1
{txt}
{com}. quietly: ologit  race2  trump age squaredage female domicil child minority edu hincfel  uemp3m voting i.country [pweight= _webal*dweight] if election2016==1 & interval==1, cluster(country)              
{txt}
{com}. est store a2
{txt}
{com}. quietly: ologit  race3  trump age squaredage female domicil child minority edu hincfel  uemp3m voting i.country [pweight= _webal*dweight] if election2016==1 & interval==1, cluster(country)              
{txt}
{com}. est store a3
{txt}
{com}. 
. 
. esttab a1 a2 a3 , keep(trump) se obslast collabels(, none)  star(* 0.10 ** 0.05 *** 0.01)  wrap    nonumbers cells(b(star fmt(3)) se(fmt(3) par)) compress  
{res}
{txt}{hline 49}
{txt}                race        race2        race3   
{txt}{hline 49}
{res}main                                             {txt}
{txt}trump     {res}     0.119***     0.124***    -0.123***{txt}
          {res}   (0.040)      (0.037)      (0.043)   {txt}
{txt}{hline 49}
{txt}N         {res}      7717         7717         7717   {txt}
{txt}{hline 49}

{com}. 
.  
. *Missing data  
. 
. clear all
{res}{txt}
{com}. use data.dta
{txt}
{com}. 
. keep if election2016==1
{txt}(27,848 observations deleted)

{com}. 
. gen miss=0
{txt}
{com}. replace miss=1 if race==.
{txt}(652 real changes made)

{com}. 
. 
. bysort trump: sum miss

{txt}{hline}
-> trump = 0

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}miss {c |}{res}     14,328    .0239391    .1528649          0          1

{txt}{hline}
-> trump = 1

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}miss {c |}{res}     10,669    .0289624    .1677088          0          1

{txt}
{com}. 
. quietly: logit  miss  trump age squaredage female domicil child minority edu hincfel  voting uemp3m i.country  [pweight= dweight],  cluster(country)              
{txt}
{com}. margins, dydx(  trump)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    24,313
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(miss), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0052309{col 26}{space 2} .0039208{col 37}{space 1}    1.33{col 46}{space 3}0.182{col 54}{space 4}-.0024537{col 67}{space 3} .0129155
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  
.  
. gen miss_before=0  if miss==0 & trump==0
{txt}(11,012 missing values generated)

{com}. replace miss_before=1 if miss==1 & trump==0
{txt}(343 real changes made)

{com}. 
. gen miss_after=0 if miss==0 & trump==1
{txt}(14,637 missing values generated)

{com}. replace miss_after=1 if miss==1 & trump==1
{txt}(309 real changes made)

{com}.  
.  *descriptive level shows similarity in what correlated with missingness of response on "racial bias"
.  
. pwcorr miss_before age   female domicil child minority edu hincfel  voting uemp3m,   star(.05)

             {txt}{c |} miss_b~e      age   female  domicil    child minority      edu
{hline 13}{c +}{hline 63}
 miss_before {c |} {res}  1.0000 
         {txt}age {c |} {res}  0.0322*  1.0000 
      {txt}female {c |} {res}  0.0076   0.0154*  1.0000 
     {txt}domicil {c |} {res} -0.0126   0.0656* -0.0057   1.0000 
       {txt}child {c |} {res}  0.0073   0.1547* -0.0651* -0.0082   1.0000 
    {txt}minority {c |} {res}  0.0137  -0.0923* -0.0037  -0.1065* -0.0586*  1.0000 
         {txt}edu {c |} {res} -0.0157  -0.0640*  0.0155* -0.1481* -0.1501*  0.0065   1.0000 
     {txt}hincfel {c |} {res}  0.0357*  0.0271*  0.0433* -0.0820* -0.0224*  0.1173* -0.2208*
      {txt}voting {c |} {res} -0.0198*  0.3049*  0.0005   0.0094  -0.0747* -0.1314*  0.2331*
      {txt}uemp3m {c |} {res}  0.0013   0.0646*  0.0055   0.0377*  0.0618* -0.0494*  0.0122 

             {txt}{c |}  hincfel   voting   uemp3m
{hline 13}{c +}{hline 27}
     hincfel {c |} {res}  1.0000 
      {txt}voting {c |} {res} -0.1351*  1.0000 
      {txt}uemp3m {c |} {res} -0.1925*  0.0452*  1.0000 
{txt}
{com}. pwcorr miss_after age  female domicil child minority edu hincfel  voting uemp3m,   star(.05)

             {txt}{c |} miss_a~r      age   female  domicil    child minority      edu
{hline 13}{c +}{hline 63}
  miss_after {c |} {res}  1.0000 
         {txt}age {c |} {res}  0.0208*  1.0000 
      {txt}female {c |} {res}  0.0245*  0.0154*  1.0000 
     {txt}domicil {c |} {res} -0.0451*  0.0656* -0.0057   1.0000 
       {txt}child {c |} {res} -0.0110   0.1547* -0.0651* -0.0082   1.0000 
    {txt}minority {c |} {res}  0.0131  -0.0923* -0.0037  -0.1065* -0.0586*  1.0000 
         {txt}edu {c |} {res} -0.0033  -0.0640*  0.0155* -0.1481* -0.1501*  0.0065   1.0000 
     {txt}hincfel {c |} {res}  0.0407*  0.0271*  0.0433* -0.0820* -0.0224*  0.1173* -0.2208*
      {txt}voting {c |} {res} -0.0303*  0.3049*  0.0005   0.0094  -0.0747* -0.1314*  0.2331*
      {txt}uemp3m {c |} {res} -0.0045   0.0646*  0.0055   0.0377*  0.0618* -0.0494*  0.0122 

             {txt}{c |}  hincfel   voting   uemp3m
{hline 13}{c +}{hline 27}
     hincfel {c |} {res}  1.0000 
      {txt}voting {c |} {res} -0.1351*  1.0000 
      {txt}uemp3m {c |} {res} -0.1925*  0.0452*  1.0000 
{txt}
{com}. 
. quietly: ebalance  trump age  female domicil child minority edu hincfel uemp3m voting country, tar(3)
{txt}
{com}. quietly: logit  miss_before    age squaredage female domicil child minority edu hincfel  voting uemp3m i.country  [iweight= dweight*_webal]  
{txt}
{com}. est store before         
{txt}
{com}. quietly: logit  miss_after    age squaredage female domicil child minority edu hincfel  voting uemp3m i.country  [iweight= dweight*_webal]     
{txt}
{com}. est store after
{txt}
{com}. 
. quietly:suest before after  ,   cluster(country)      
{txt}
{com}. 
. *Tests in Table "missing data" in the Appendix
. 
. test [before_miss_before]age -  [after_miss_after]age = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]age - [after_miss_after]age = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.71
{txt}{col 10}Prob > chi2 =  {res}  0.3993
{txt}
{com}. test [before_miss_before]female-  [after_miss_after]female = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]female - [after_miss_after]female = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    2.28
{txt}{col 10}Prob > chi2 =  {res}  0.1315
{txt}
{com}. test [before_miss_before]domicil-  [after_miss_after]domicil = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]domicil - [after_miss_after]domicil = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.04
{txt}{col 10}Prob > chi2 =  {res}  0.8510
{txt}
{com}. test [before_miss_before]child-  [after_miss_after]child = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]child - [after_miss_after]child = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.28
{txt}{col 10}Prob > chi2 =  {res}  0.5993
{txt}
{com}. test [before_miss_before]minority-  [after_miss_after]minority = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]minority - [after_miss_after]minority = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    1.04
{txt}{col 10}Prob > chi2 =  {res}  0.3070
{txt}
{com}. test [before_miss_before]edu-  [after_miss_after]edu = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]edu - [after_miss_after]edu = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.21
{txt}{col 10}Prob > chi2 =  {res}  0.6458
{txt}
{com}. test [before_miss_before]hincfel-  [after_miss_after]hincfel = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]hincfel - [after_miss_after]hincfel = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.18
{txt}{col 10}Prob > chi2 =  {res}  0.6680
{txt}
{com}. test [before_miss_before]voting-  [after_miss_after]voting = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]voting - [after_miss_after]voting = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.87
{txt}{col 10}Prob > chi2 =  {res}  0.3496
{txt}
{com}. test [before_miss_before]uemp3m-  [after_miss_after]uemp3m = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]uemp3m - [after_miss_after]uemp3m = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.36
{txt}{col 10}Prob > chi2 =  {res}  0.5483
{txt}
{com}. test [before_miss_before]voting-  [after_miss_after]voting = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[before_miss_before]voting - [after_miss_after]voting = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.87
{txt}{col 10}Prob > chi2 =  {res}  0.3496
{txt}
{com}. 
. 
. *Treeatment effects with varying Time intervals 
. 
. 
. clear all
{res}{txt}
{com}. use data.dta
{txt}
{com}. 
. keep if election2016==1
{txt}(27,848 observations deleted)

{com}. 
. 
. quietly: ebalance  trump  age squaredage female domicil child minority edu hincfel  voting country if edate<d(13nov2016) & edate>d(4nov2016), tar(3)
{txt}
{com}. quietly: ologit  race  trump age squaredage female domicil child minority edu hincfel voting i.country [pweight= _webal*dweight] if edate<d(13nov2016) & edate>d(4nov2016), cluster(country) 
{txt}
{com}. quietly: margins, dydx(trump) predict(outcome(1))  post
{txt}
{com}. est store _5_days
{txt}
{com}. quietly: ebalance  trump  age squaredage female domicil child minority edu hincfel  voting country if edate<d(18nov2016) & edate>d(30oct2016)
{txt}
{com}. quietly: ologit  race  trump age squaredage female domicil child minority edu hincfel voting i.country [pweight= _webal*dweight] if edate<d(18nov2016) & edate>d(30oct2016), cluster(country) 
{txt}
{com}. quietly: margins, dydx(trump)  predict(outcome(1)) post
{txt}
{com}. est store _10_days
{txt}
{com}. quietly: ebalance  trump  age squaredage female domicil child minority edu hincfel  voting country if edate<d(23nov2016) & edate>d(25oct2016)
{txt}
{com}. quietly:ologit  race  trump age squaredage female domicil child minority edu hincfel voting i.country [pweight= _webal*dweight] if edate<d(23nov2016) & edate>d(25oct2016), cluster(country) 
{txt}
{com}. quietly:margins, dydx(trump) predict(outcome(1)) post
{txt}
{com}. est store _15_days
{txt}
{com}. quietly: ebalance  trump  age squaredage female domicil child minority edu hincfel  voting country if edate<d(28nov2016) & edate>d(20oct2016)
{txt}
{com}. quietly: ologit  race  trump age squaredage female domicil child minority edu hincfel voting i.country [pweight= _webal*dweight] if edate<d(28nov2016) & edate>d(20oct2016), cluster(country)
{txt}
{com}. quietly: margins, dydx(trump) predict(outcome(1)) post
{txt}
{com}. est store _20_days
{txt}
{com}. quietly: ebalance  trump  age squaredage female domicil child minority edu hincfel  voting country if edate<d(3dec2016) & edate>d(15oct2016)
{txt}
{com}. quietly: ologit  race  trump age squaredage female domicil child minority edu hincfel voting i.country [pweight= _webal*dweight] if edate<d(3dec2016) & edate>d(15oct2016), cluster(country) 
{txt}
{com}. quietly: margins, dydx(trump) predict(outcome(1)) post
{txt}
{com}. est store _25_days
{txt}
{com}. quietly: ebalance  trump  age squaredage female domicil child minority edu hincfel  voting country if edate<d(8dec2016) & edate>d(10oct2016)
{txt}
{com}. quietly: ologit  race  trump age squaredage female domicil child minority edu hincfel voting i.country [pweight= _webal*dweight] if edate<d(8dec2016) & edate>d(10oct2016), cluster(country) 
{txt}
{com}. quietly: margins, dydx(trump) predict(outcome(1)) post
{txt}
{com}. est store _30_days
{txt}
{com}. quietly: ebalance  trump  age squaredage female domicil child minority edu hincfel  voting country if edate<d(23dec2016) & edate>d(26sep2016)
{txt}
{com}. quietly: ologit  race  trump age squaredage female domicil child minority edu hincfel voting i.country [pweight= _webal*dweight] if edate<d(23dec2016) & edate>d(26sep2016), cluster(country) 
{txt}
{com}. quietly: margins, dydx(trump) predict(outcome(1)) post
{txt}
{com}. est store _45_days
{txt}
{com}. quietly: ebalance  trump  age squaredage female domicil child minority edu hincfel  voting country if edate<d(7jan2017) & edate>d(11sep2016)
{txt}
{com}. quietly: ologit  race  trump age squaredage female domicil child minority edu hincfel voting i.country [pweight= _webal*dweight] if edate<d(7jan2017) & edate>d(11sep2016), cluster(country) 
{txt}
{com}. quietly: margins, dydx(trump) predict(outcome(1)) post
{txt}
{com}. est store _60_days
{txt}
{com}. 
. coefplot (_5_days, label(one) pstyle(p3)) (_10_days, label(two)  pstyle(p4))  (_15_days, label(three)  pstyle(p5)) (_20_days, label(four)  pstyle(p6))  (_25_days, label(five)  pstyle(p7)) (_30_days, label(six)  pstyle(p8)) (_45_days, label(seven)  pstyle(p9)) (_60_days, label(eight)  pstyle(p10)) , vertical legend(off) keep(trump) xlabel("") yline(0) msymbol(S) title("Treatment effects for varying time bandwidths (5-60 days)" )
{res}{txt}
{com}. 
. *By country
. 
. clear all
{res}{txt}
{com}. use data.dta
{txt}
{com}. 
. keep if interval==1
{txt}(44,774 observations deleted)

{com}. 
. gen at=0
{txt}
{com}. gen be=0
{txt}
{com}. gen ch=0
{txt}
{com}. gen cz=0
{txt}
{com}. gen de=0
{txt}
{com}. gen ee=0
{txt}
{com}. gen fi=0
{txt}
{com}. gen il=0
{txt}
{com}. gen uk=0
{txt}
{com}. gen nl=0
{txt}
{com}. gen no=0
{txt}
{com}. gen si=0
{txt}
{com}. gen se=0
{txt}
{com}. 
. replace at=1 if country==1
{txt}(780 real changes made)

{com}. replace be=1 if country==2
{txt}(408 real changes made)

{com}. replace ch=1 if country==3
{txt}(319 real changes made)

{com}. replace cz=1 if country==4
{txt}(1,981 real changes made)

{com}. replace de=1 if country==5
{txt}(651 real changes made)

{com}. replace ee=1 if country==6
{txt}(691 real changes made)

{com}. replace fi=1 if country==7
{txt}(641 real changes made)

{com}. replace il=1 if country==11
{txt}(589 real changes made)

{com}. replace uk=1 if country==9
{txt}(474 real changes made)

{com}. replace nl=1 if country==13
{txt}(427 real changes made)

{com}. replace no=1 if country==14
{txt}(445 real changes made)

{com}. replace si=1 if country==17
{txt}(323 real changes made)

{com}. replace se=1 if country==18
{txt}(342 real changes made)

{com}.  
. 
. gen interat = trump*at
{txt}
{com}. gen interbe = trump*be
{txt}
{com}. gen interch = trump*ch
{txt}
{com}. gen intercz = trump*cz
{txt}
{com}. gen interde = trump*de
{txt}
{com}. gen interee = trump*ee
{txt}
{com}. gen interfi = trump*fi
{txt}
{com}. gen interuk = trump*uk
{txt}
{com}. gen interil = trump*il
{txt}
{com}. gen internl = trump*nl
{txt}
{com}. gen interno = trump*no
{txt}
{com}. gen interse = trump*se
{txt}
{com}. gen intersi = trump*si
{txt}
{com}. 
. 
. ebalance  trump age  female domicil child minority edu hincfel uemp3m voting country, tar(3)
{res}

Data Setup
{txt}Treatment variable:   {res}trump
{txt}Covariate adjustment:{res} age female domicil child minority edu hincfel uemp3m voting country {txt}(1st order).{res} age female domicil child minority edu hincfel uemp3m voting country {txt}(2nd order).{res}{res} age female domicil child minority edu hincfel uemp3m voting country {txt}(3rd order).


{res}Optimizing...
{txt}Iteration 1: Max Difference = {res}54211.0791{txt}
{txt}Iteration 2: Max Difference = {res}19940.6168{txt}
{txt}Iteration 3: Max Difference = {res}7333.21878{txt}
{txt}Iteration 4: Max Difference = {res}2695.21764{txt}
{txt}Iteration 5: Max Difference = {res}988.99616{txt}
{txt}Iteration 6: Max Difference = {res}361.322643{txt}
{txt}Iteration 7: Max Difference = {res}130.442267{txt}
{txt}Iteration 8: Max Difference = {res}45.5805665{txt}
{txt}Iteration 9: Max Difference = {res}14.5556715{txt}
{txt}Iteration 10: Max Difference = {res}3.60850173{txt}
{txt}Iteration 11: Max Difference = {res}.604735376{txt}
{txt}Iteration 12: Max Difference = {res}.020105958{txt}
{txt}Iteration 13: Max Difference = {res}.000015188{txt}
{txt}maximum difference smaller than the tolerance level; {res}convergence achieved


Treated units: {txt}3716{col 24}{res}total of weights: {txt}3716
{res}Control units: {txt}4131{col 24}{res}total of weights: {txt}3716


{res}Before: {txt}without weighting
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    48.26}}}{space 1}{space 1}{ralign 9:{res:{sf:    324.9}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03768}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    48.68}}}{space 1}{space 1}{ralign 9:{res:{sf:    346.5}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06971}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .5221}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2496}}}{space 1}{space 1}{ralign 9:{res:{sf:  -.08835}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .5146}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2498}}}{space 1}{space 1}{ralign 9:{res:{sf:  -.05861}}}{space 1}
{space 0}{space 0}{ralign 12:domicil}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    2.737}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.616}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.1551}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    2.848}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.575}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.2632}}}{space 1}
{space 0}{space 0}{ralign 12:child}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.682}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2168}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.7839}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.684}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2161}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.7932}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .05463}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05166}}}{space 1}{space 1}{ralign 9:{res:{sf:     3.92}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .05277}}}{space 1}{space 1}{ralign 9:{res:{sf:      .05}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.001}}}{space 1}
{space 0}{space 0}{ralign 12:edu}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.138}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.904}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1755}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.048}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.003}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2193}}}{space 1}
{space 0}{space 0}{ralign 12:hincfel}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.869}}}{space 1}{space 1}{ralign 9:{res:{sf:     .635}}}{space 1}{space 1}{ralign 9:{res:{sf:     .708}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.881}}}{space 1}{space 1}{ralign 9:{res:{sf:    .6614}}}{space 1}{space 1}{ralign 9:{res:{sf:    .7451}}}{space 1}
{space 0}{space 0}{ralign 12:uemp3m}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.744}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1905}}}{space 1}{space 1}{ralign 9:{res:{sf:   -1.119}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.733}}}{space 1}{space 1}{ralign 9:{res:{sf:     .196}}}{space 1}{space 1}{ralign 9:{res:{sf:   -1.051}}}{space 1}
{space 0}{space 0}{ralign 12:voting}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6991}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2104}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.8684}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .696}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2117}}}{space 1}{space 1}{ralign 9:{res:{sf:    -.852}}}{space 1}
{space 0}{space 0}{ralign 12:country}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    6.711}}}{space 1}{space 1}{ralign 9:{res:{sf:    22.15}}}{space 1}{space 1}{ralign 9:{res:{sf:    .9165}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    7.242}}}{space 1}{space 1}{ralign 9:{res:{sf:    23.79}}}{space 1}{space 1}{ralign 9:{res:{sf:    .8027}}}{space 1}


{res}After:  {txt}_webal as the weighting variable
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    48.26}}}{space 1}{space 1}{ralign 9:{res:{sf:    324.9}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03768}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    48.26}}}{space 1}{space 1}{ralign 9:{res:{sf:    324.9}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03772}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .5221}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2496}}}{space 1}{space 1}{ralign 9:{res:{sf:  -.08835}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .5221}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2496}}}{space 1}{space 1}{ralign 9:{res:{sf:  -.08835}}}{space 1}
{space 0}{space 0}{ralign 12:domicil}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    2.737}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.616}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.1551}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    2.737}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.616}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.1551}}}{space 1}
{space 0}{space 0}{ralign 12:child}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.682}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2168}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.7839}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.682}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2168}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.7838}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .05463}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05166}}}{space 1}{space 1}{ralign 9:{res:{sf:     3.92}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .05463}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05166}}}{space 1}{space 1}{ralign 9:{res:{sf:     3.92}}}{space 1}
{space 0}{space 0}{ralign 12:edu}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.138}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.904}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1755}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    4.138}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.904}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1755}}}{space 1}
{space 0}{space 0}{ralign 12:hincfel}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.869}}}{space 1}{space 1}{ralign 9:{res:{sf:     .635}}}{space 1}{space 1}{ralign 9:{res:{sf:     .708}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.869}}}{space 1}{space 1}{ralign 9:{res:{sf:     .635}}}{space 1}{space 1}{ralign 9:{res:{sf:    .7081}}}{space 1}
{space 0}{space 0}{ralign 12:uemp3m}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.744}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1905}}}{space 1}{space 1}{ralign 9:{res:{sf:   -1.119}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    1.744}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1905}}}{space 1}{space 1}{ralign 9:{res:{sf:   -1.119}}}{space 1}
{space 0}{space 0}{ralign 12:voting}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6991}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2104}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.8684}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6991}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2104}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.8684}}}{space 1}
{space 0}{space 0}{ralign 12:country}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    6.711}}}{space 1}{space 1}{ralign 9:{res:{sf:    22.15}}}{space 1}{space 1}{ralign 9:{res:{sf:    .9165}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    6.711}}}{space 1}{space 1}{ralign 9:{res:{sf:    22.15}}}{space 1}{space 1}{ralign 9:{res:{sf:    .9165}}}{space 1}
{res}{txt}
{com}. 
. quietly: reg race  trump interat age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interat)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interat}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0202876{col 26}{space 2}  .008114{col 37}{space 1}    2.50{col 46}{space 3}0.028{col 54}{space 4} .0026086{col 67}{space 3} .0379666
{txt}{space 5}interat {c |}{col 14}{res}{space 2} .0250048{col 26}{space 2} .0073483{col 37}{space 1}    3.40{col 46}{space 3}0.005{col 54}{space 4} .0089944{col 67}{space 3} .0410153
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump interbe age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interbe)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interbe}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2}   .02287{col 26}{space 2} .0080878{col 37}{space 1}    2.83{col 46}{space 3}0.015{col 54}{space 4} .0052481{col 67}{space 3} .0404919
{txt}{space 5}interbe {c |}{col 14}{res}{space 2} .0037618{col 26}{space 2} .0086681{col 37}{space 1}    0.43{col 46}{space 3}0.672{col 54}{space 4}-.0151244{col 67}{space 3} .0226479
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump interch age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interch)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interch}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0232238{col 26}{space 2} .0078613{col 37}{space 1}    2.95{col 46}{space 3}0.012{col 54}{space 4} .0060955{col 67}{space 3} .0403521
{txt}{space 5}interch {c |}{col 14}{res}{space 2} -.003635{col 26}{space 2} .0076919{col 37}{space 1}   -0.47{col 46}{space 3}0.645{col 54}{space 4}-.0203941{col 67}{space 3} .0131242
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump intercz age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump intercz)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump intercz}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0195893{col 26}{space 2} .0093418{col 37}{space 1}    2.10{col 46}{space 3}0.058{col 54}{space 4}-.0007647{col 67}{space 3} .0399433
{txt}{space 5}intercz {c |}{col 14}{res}{space 2} .0139974{col 26}{space 2} .0094748{col 37}{space 1}    1.48{col 46}{space 3}0.165{col 54}{space 4}-.0066464{col 67}{space 3} .0346411
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump interde age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interde)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interde}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0225325{col 26}{space 2} .0082378{col 37}{space 1}    2.74{col 46}{space 3}0.018{col 54}{space 4} .0045838{col 67}{space 3} .0404812
{txt}{space 5}interde {c |}{col 14}{res}{space 2} .0076635{col 26}{space 2} .0085064{col 37}{space 1}    0.90{col 46}{space 3}0.385{col 54}{space 4}-.0108704{col 67}{space 3} .0261975
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump interee age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interee)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interee}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0261013{col 26}{space 2}  .007305{col 37}{space 1}    3.57{col 46}{space 3}0.004{col 54}{space 4} .0101851{col 67}{space 3} .0420174
{txt}{space 5}interee {c |}{col 14}{res}{space 2}  -.03444{col 26}{space 2}  .007592{col 37}{space 1}   -4.54{col 46}{space 3}0.001{col 54}{space 4}-.0509814{col 67}{space 3}-.0178985
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump interfi age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interfi)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interfi}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0275826{col 26}{space 2} .0061935{col 37}{space 1}    4.45{col 46}{space 3}0.001{col 54}{space 4} .0140882{col 67}{space 3}  .041077
{txt}{space 5}interfi {c |}{col 14}{res}{space 2}-.0563129{col 26}{space 2} .0061599{col 37}{space 1}   -9.14{col 46}{space 3}0.000{col 54}{space 4}-.0697342{col 67}{space 3}-.0428916
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump interuk age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interuk)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interuk}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0238042{col 26}{space 2} .0078518{col 37}{space 1}    3.03{col 46}{space 3}0.010{col 54}{space 4} .0066966{col 67}{space 3} .0409118
{txt}{space 5}interuk {c |}{col 14}{res}{space 2}-.0135499{col 26}{space 2} .0075186{col 37}{space 1}   -1.80{col 46}{space 3}0.097{col 54}{space 4}-.0299314{col 67}{space 3} .0028316
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump interil age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interil)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interil}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0208353{col 26}{space 2} .0079689{col 37}{space 1}    2.61{col 46}{space 3}0.023{col 54}{space 4} .0034726{col 67}{space 3}  .038198
{txt}{space 5}interil {c |}{col 14}{res}{space 2} .0298136{col 26}{space 2} .0074832{col 37}{space 1}    3.98{col 46}{space 3}0.002{col 54}{space 4} .0135091{col 67}{space 3} .0461181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump internl age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump internl)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump internl}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0227487{col 26}{space 2} .0080449{col 37}{space 1}    2.83{col 46}{space 3}0.015{col 54}{space 4} .0052203{col 67}{space 3} .0402771
{txt}{space 5}internl {c |}{col 14}{res}{space 2} .0068818{col 26}{space 2} .0090394{col 37}{space 1}    0.76{col 46}{space 3}0.461{col 54}{space 4}-.0128133{col 67}{space 3} .0265769
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump interno age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interno)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interno}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2}  .020733{col 26}{space 2} .0079105{col 37}{space 1}    2.62{col 46}{space 3}0.022{col 54}{space 4} .0034974{col 67}{space 3} .0379685
{txt}{space 5}interno {c |}{col 14}{res}{space 2} .0438212{col 26}{space 2} .0084633{col 37}{space 1}    5.18{col 46}{space 3}0.000{col 54}{space 4} .0253812{col 67}{space 3} .0622612
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump interse age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump interse)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump interse}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0249003{col 26}{space 2} .0074586{col 37}{space 1}    3.34{col 46}{space 3}0.006{col 54}{space 4} .0086493{col 67}{space 3} .0411513
{txt}{space 5}interse {c |}{col 14}{res}{space 2}-.0505555{col 26}{space 2} .0070949{col 37}{space 1}   -7.13{col 46}{space 3}0.000{col 54}{space 4} -.066014{col 67}{space 3} -.035097
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly: reg race  trump intersi age squaredage edu hincfel uemp3m female child domicil minority voting i.country [pweight= _webal*dweight], cluster(country)
{txt}
{com}. margins, dydx(  trump intersi)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     7,717
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:trump intersi}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trump {c |}{col 14}{res}{space 2} .0240965{col 26}{space 2} .0077404{col 37}{space 1}    3.11{col 46}{space 3}0.009{col 54}{space 4} .0072317{col 67}{space 3} .0409614
{txt}{space 5}intersi {c |}{col 14}{res}{space 2}-.0278886{col 26}{space 2} .0083564{col 37}{space 1}   -3.34{col 46}{space 3}0.006{col 54}{space 4}-.0460955{col 67}{space 3}-.0096816
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
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. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\k1801607\Dropbox\Trump2_accepted\supplementary.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 4 Jul 2019, 18:13:10
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{txt}{sf}{ul off}